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# Postdoctoral Research Associate - Coastal Landscape Geospatial Modeling

- **Company:** Oak Ridge National Laboratory
- **Location:** Oak Ridge, TN, United States
- **Contract:** Temporary to permanent
- **Skills:** Artificial Intelligence, Computer Programming, Data Integration, Fortran (Programming Language), Python (Programming Language), Machine Learning, Pattern Recognition, Surface Modeling
- **Published:** September 1, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/88364414/1

## About the Role

* A Ph.D. in Earth sciences, environmental sciences, atmospheric sciences, or a closely related field, either already completed or expected to be completed within the next six months.
 * Experience in ecological modelling and/or Land Surface Modelling
 * Knowledge and experience working in HPC environment.
 * Programming experience in Fortran, Python or R
 * Experience in manipulating and analyzing large observation data or model outputs
 * Working knowledge of terrestrial biogeochemistry and nutrient-cycling processes
 * Strong communication skills, * Experience with the land model intheEnergy Exascale Earth Model(E3SM) or Community Earth System Model (CESM)
 * Background in peatland, wetland, or high-latitude ecosystem biogeochemistry.
 * Experience applying machine-learning or AI methods to environmental data analysis, model calibration, or model-data integration.
 * Experience synthesizing multi-source, multi-scale observational datasets.

## Description

* Develop, implement, and test new and improved process representations of nutrient (nitrogen and phosphorus) cycling and their coupling to hydrological and ecosystem processes within ELM and ELM-Peatlands.
 * Evaluate model performance against observational and experimental datasets, including data from SPRUCE, AmeriFlux towers, and other observational and experimental
 * Compile, harmonize, and synthesize multi-source observational datasets (e.g., flux, soil biogeochemistry, plant trait, and remote-sensing data) to support model benchmarking, parameterization, and uncertainty quantification.
 * Explore and apply AI/ML approaches (e.g., machine-learning emulators, surrogate modeling, AI-assisted model calibration, or pattern detection in large observational datasets) to improve nutrient-cycling representation and reduce predictive uncertainty in models.
 * Collaborate with an interdisciplinary team of ecologists, biogeochemists, modelers and data scientists across ORNL and the broader DOE E3SM and Environmental System Science communities.
 * Present research findings at scientific conferences and prepare manuscripts for peer-reviewed publication.


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